Traditional fault detection methods use model-based or knowledge-based approaches that require considerable effort to design and build, and incorporate analytical models or knowledge-based systems. In order to address the difficulties that lie in model-based or knowledge-based methods, model-free statistical process monitoring (SPM) techniques have been developed, which require only a good historical data set of normal operations. In current manufacturing and industrial processes, massive amounts of trace or machine data are generated and recorded. Because of the high dimensionality of the data, both principal component analysis (PCA) and partial least squares (PLS) based multivariate statistical fault detection techniques are often used to monitor continuous processes.
In one prior art approach, multivariate statistical process control (MSPC) techniques for process monitoring and fault diagnosis based on principal-component analysis (PCA) models of multi-scale data have been implemented. Process measurements, representing the cumulative effects of many underlying process phenomena, can be decomposed by applying multi-resolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales and PCA is applied to the multi-scale data to capture process variable correlations occurring at different scales. Selecting an ortho-normal mother wavelet allows each principal component to be implemented as a function of the process variables at only one scale level. Once a fault is detected, the contributions of the variations at each scale to the fault can be computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, however, one can further compute the variable contributions to those scales.
Other prior art techniques involve monitoring a process through the use of PCA only. Correlated attributes can be measured for the process to be monitored (the production process). A PCA algorithm can then be performed on the measured correlated attributes so as to generate one or more production principal components, which can then be compared to a principal component associated with a calibration process (i.e., a calibration principal component). The calibration principal component is obtained by measuring correlated attributes of a calibration process and by performing a PCA on the measured correlated attributes so as to generate one or more principal components. A principal component having a feature indicative of a desired process state, process event and/or chamber state is then identified and designated as the calibration principal component.
Abnormal situations commonly result from the failure of field devices such as instrumentation, control valves, and pumps or some form of process disturbance that causes the plant operations to deviate from the normal operating state. In particular, the undetected failure of key instrumentation and other devices, which are part of the process control system, can cause the control system to drive the process into an undesirable and dangerous state. Early detection of these failures enables the operation team to intervene before the control system escalates the failure into a more severe incident.
Thousands of process and equipment measurements are gathered by modern digital process control systems and deployed in refineries and chemical plants. Several years of such data can be stored as histories in databases for analysis and reporting. These databases can then be mined for the data patterns that occur during normal operation and those patterns can be used to determine the abnormal behavior of the process.
The aforementioned prior art techniques reply upon the use of a covariance matrix to construct a model of variable relationships in PCA and also use static methods that compare the consistency of correlations between variables for a given time stamp. There is a need, however, for efficiently and effectively monitoring process dynamics in an industrial or manufacturing setting. Additionally, because time-series synchronization occurs in the context of a data pre-processing operation, the use of a snapshot monitoring method does not take into account the synchronization of time-series data corresponding to individual tags. Moreover, delayed time stamps used as inputs to PCA modules are not sufficient for clearly capturing the process dynamics.
Based on the foregoing it is believed that a need exists for an improved technique for consistently monitoring and detecting faults in manufacturing and industrial processes. Additionally, a need exists for comparing the consistency of process dynamics (e.g., changes in time) in order to improve the performance of system monitoring and preventing incidents in manufacturing settings.